How can I improve multi-agent RL collaboration with limited information?
Double Distillation Network for Multi-Agent Reinforcement Learning
This paper introduces the Double Distillation Network (DDN), a novel approach for improving multi-agent reinforcement learning in partially observable environments. DDN uses two distillation modules: one to bridge the gap between centralized training and decentralized execution, and another to encourage exploration by incorporating intrinsic rewards derived from global state information. This addresses a common problem in multi-agent systems where agents struggle to coordinate effectively when they can't see the entire environment.
Key points for LLM-based multi-agent systems: DDN's focus on decentralized execution aligns with the need for independent LLMs to act autonomously. The knowledge distillation method used in DDN could be adapted to improve the consistency between individually acting LLMs and an overall system goal, enabling better coordination. The use of intrinsic rewards related to global state information could be relevant for motivating LLMs to explore new dialogue strategies and improve collaborative output. The personalization aspect of the fusion blocks within DDN offers a potential mechanism for tailoring global information to individual LLM agents, enabling them to act more effectively based on their specific roles or perspectives.